Effect of systematic nursing on scar appearance following radical thyroidectomy: a retrospective cohort analysis
Bibliographic record
Abstract
Female patients with thyroid cancer have a greater desire for not only curing the disease but also leaving an inconspicuous scar. Systematic nursing has a comprehensive effect on reducing scar formation. However, comprehensive nursing guidance for postoperative scar management are not usually available for patients with thyroid cancer. This study aimed to compare the comprehensive effect of systematic nursing compared to traditional nursing measures on postoperative scar management in patients with thyroid cancer. One hundred patients who underwent radical thyroidectomy between January 2019 and June 2019 were selected and randomly assigned to two groups. Patients in the experimental group (n = 50) received nursing care under systematic nursing guidance, while those in the control group (n = 50) received traditional nursing care. In the 3rd, 6th, and 12th months after surgery, all patients returned to the general surgery department for scar evaluation. Scarring was evaluated using the Vancouver Scar Scale (VSS). Patients' self-evaluated satisfaction scores for scar appearance and nursing guidance were also collected. Compared with the control group, scars in the experimental group were less inconspicuous. At the 6th and 12th months after surgery, the total VSS score in the experimental group was significantly lower than that in the control group (P < 0.001). By the 12th month after surgery, patients in the experimental group were more satisfied with the final scar appearance (experimental group: 4.69 ± 0.48; control group: 4.06 ± 0.57; P < 0.01) and nursing care (experimental group: 4.88 ± 0.34; control group: 4.50 ± 0.52; P < 0.05). Systematic nursing guidance had positive effects on reducing scar formation and enhancing patient satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".